Function2Form Bridge-Toward synthetic protein holistic performance prediction.
antibody screening
community-based data reporting
de novo protein design
high throughput in silico screening
in silico modeling
machine learning
protein scoring
synthetic biology
Journal
Proteins
ISSN: 1097-0134
Titre abrégé: Proteins
Pays: United States
ID NLM: 8700181
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
25
06
2019
revised:
02
09
2019
accepted:
17
09
2019
pubmed:
8
10
2019
medline:
5
1
2021
entrez:
8
10
2019
Statut:
ppublish
Résumé
Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.
Substances chimiques
Antibodies
0
ClfA protein, Staphylococcus aureus
0
Coagulase
0
MUC1 protein, human
0
Mucin-1
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
462-475Subventions
Organisme : Science Foundation Ireland
ID : 12/RC/2273
Pays : Ireland
Organisme : Science Foundation Ireland
ID : 15/CDA/3630
Pays : Ireland
Organisme : Health Research Board
ID : MRCG2016‐25
Pays : Ireland
Informations de copyright
© 2019 Wiley Periodicals, Inc.
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